Professor Mihaela van der Schaar
Mihaela van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Turing Faculty Fellow at The Alan Turing Institute in London, a Chancellor’s Professor at UCLA and an IEEE Fellow.
Professor Andres Floto
Andres Floto is a Professor of Respiratory Biology at the University of Cambridge, a Wellcome Trust Senior Investigator, and Director of the UK Cystic Fibrosis Innovation Hub.
Dr Sarah Teichmann FMedSci FRS
Head of Cellular Genetics and Senior Group Leader, Wellcome Sanger Institute
Dr Teichmann is a world-leading scientist who combines her expertise in computational and systems biology with single-cell biology, genomics and immunology. She applies her knowledge using novel approaches to answer questions fundamental to our understanding of biology and medicine.
Dr Ari Ercole
Consultant and Researcher in anaesthesia and intensive care • Fellow in Clinical Medicine at Magdalene College, University of Cambridge
Dr Ercole is a consultant in anaesthesia and intensive care medicine. He also holds a PhD in experimental physics from the University of Cambridge. He divides his time between clinical practice and research.
Professor Stefan Scholtes
Dennis Gillings Professor of Health Management, Cambridge Judge Business School • Director of the Centre for Health Leadership & Enterprise
Professor Scholtes‘s research is strongly practice-based and embedded in close collaborations with the Cambridge University Hospitals NHS Foundation Trust, Cambridgeshire and Peterborough Foundation Trust, and Public Health England.
Dr Eoin McKinney
University Lecturer in Renal medicine at the University of Cambridge • Honorary consultant in nephrology and transplantation, Cambridge University Hospitals NHS Foundation Trust
Dr McKinney’s research explores the interface between immune responses to infection and those driving inflammatory pathology, applying machine learning methods to the integration of multi-omics data, building interpretable predictive models for rapid translation into clinical practice while informing underlying disease biology and identifying novel therapeutic strategies.
Dr Alexander Gimson
Consultant Transplant Hepatologist, Cambridge University Hospitals NHS Foundation Trust • Chair of the Care Advisory Group, Cambridgeshire & Peterborough Sustainability and Transformation Partnership
Dr Gimson led the national team which developed in a new organ allocation offering scheme whereby organs are offered to the person on a national waiting list who has the greatest calculated net life years gained from the particular donor organ.
He is running a project which aims to discover if an AI/machine learning model can beat existing models, to make that organ-offering even more equitable.
Dr Angela Wood
Professor of Health Data Science at the University of Cambridge
Dr Wood‘s research interests are centred on the development and application of statistical methods for advancing epidemiological research. She has focused on developing statistical methodology for handling measurement error, using repeated measures of risk factors, missing data problems, multiple imputation, risk prediction and meta-analysis.
Professor Pietro Liò
Professor of Computational Biology in the Department of Computer Science at the University of Cambridge • Member of the Artificial Intelligence group of the Computer Laboratory
Professor Liò has PhDs in Complex Systems and Non Linear Dynamics, and in Theoretical Genetics. He is the author of over 400 papers. His specialties include bioinformatics algorithms, predictive models in personalised medicine, modeling comorbidity and aging, methods for combining multi-scale biological processes, statistics of multi omics and multi physics modelling of molecules-cell-tissue-organ interactions.
Dr José Miguel Hernández-Lobato
University Lecturer (US equivalent to Assistant Professor) in Machine Learning at the University of Cambridge
Dr Hernández-Lobato‘s research revolves around model-based machine learning with a focus on probabilistic learning techniques and with a particular interest on Bayesian optimisation, matrix factorization methods, copulas, Gaussian processes and sparse linear models.
Namshik Han is Head of Computational Research & AI at the Milner Therapeutics Institute within the University of Cambridge. He is also the Co-Founder of KURE.ai.
Dr Adrian Weller MBE is a Director of Research in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. He is a Programme Director and Turing Fellow leading work on Safe and Ethical AI at The Alan Turing Institute, the UK national institute for data science and AI. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the advisory board for the Government’s Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.
Dr George Mells is a Consultant Hepatologist at Addenbrooke’s Hospital, and a theme lead for UK-PBC. UK-PBC has established the world’s largest patient cohort with deep phenotype, genotype data and disease outcome data. He will use this unique cohort for statistical modelling of disease to derive clinical prediction models and inform the design and interpretation of ‘omic experiments aimed at re-purposing of drugs and development of predictive biomarkers. Dr Mells will be collaborating with Dr Brian Tom and Dr Chris Wallace in the MRC Biostatistics Unit.
Richard Peck spent over 30 years as a clinical pharmacologist in the pharmaceutical industry and was Global head of Clinical Pharmacology at Roche for the last thirteen of these. Since retiring from Roche, he has been appointed Honorary Professor of Pharmacology & Therapeutics at the University of Liverpool.
His research interests include understanding and utilising variability in drug response to enable precision dosing; applying clinical pharmacology to enable the development of personalised/stratified medicines and the use of model-based drug development strategies.
Qingyuan Zhao is a University Assistant Professor in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics (DPMMS) at University of Cambridge and a Turing Fellow at the Alan Turing Institute.
He is interested in improving the quality and appraisal of statistical research, including new methodology and a better understanding of causal inference, novel study designs, sensitivity analysis, multiple testing, and selective inference. His substantive research focuses on causal inference problems arising in genetics and epidemiology.
Evgeny is one of the centre’s research engineers, and has been a part-time PhD student at the van der Schaar lab since 2021. His educational background is Natural Sciences at the University of Cambridge, followed by postgraduate study in Computer Science at University of Southampton.
Evgeny was an AI Resident at Microsoft Research Cambridge before joining the lab, where he worked on projects covering meta-learning and reinforcement learning as applied to recommender systems. He also has experience in computational finance, having worked in a fintech start-up and commodities trading.
Evgeny facilitates turning the lab’s research code into robust production quality code, making it more scalable, applying software engineering best practices; he also collaborates with our PhD students on some research topics.
He is particularly interested in working on AutoML and time-series modelling, as well as machine learning for time series, and synthetic data.
Rob is one of our research engineers since joining in 2022. His educational background is Physical Natural Sciences at the University of Cambridge.
Rob worked for five years at a medical software company before joining us. In his roles of Senior Data Scientist and Data Engineer, he focussed on data extraction from scientific literature, and it was here he became excited by applying Machine Learning methods in medical contexts.
Rob works to make research code robust, ensuring software engineering best practices are applied. He also creates user interfaces that demonstrate the research methods to make sure that their power can be understood by as many people as possible.
He has so far shown a great interest in the interpretability of Machine Learning methods.